Datastores can include memory, caches, and/or databases which can be configured to receive, store, and provide data such that the data can be provided in a temporally relevant, or just-in-time manner. Cache policies can include executable instructions, which can be applied to the datastores to configure memory footprints, data access permissions, read/write permissions, and the temporal availability of the data present in the datastore. Cache policies can be predicted in a machine learning process based on usage patterns associated with applications or computing environments coupled to the datastores.
Machine learning can include an application of artificial intelligence that automates the development of an analytical model by using algorithms that iteratively learn patterns from data without explicit indication of the data patterns. Machine learning can commonly be used in pattern recognition, computer vision, email filtering and optical character recognition and can enable the construction of algorithms that can accurately learn from data to predict model outputs thereby making data-driven predictions or decisions.